{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kats 204 Forecasting with Meta-Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial will introduce the meta-learning framework for forecasting in Kats.  The table of contents for Kats 204 is as follows:\n",
    "\n",
    "1. Overview of Meta-Learning Framework For Forecasting         \n",
    "2. Introduction to `GetMetaData`\n",
    "3. Determining Predictability with `MetaLearnPredictability`\n",
    "4. Model Selection with `MetaLearnModelSelect`       \n",
    "5. Hyperparameter Tuning with `MetaLearnHPT`              \n",
    "    5.1. Initializing `MetaLearnHPT`             \n",
    "    5.2. `MetaLearnHPT` with Default Neural Network Model Structure      \n",
    "    5.3. `MetaLearnHPT` with Custom Neural Network Model Structure\n",
    "\n",
    "For the details of methodology of meta-learning framework and our experiment results, please see the reference https://arxiv.org/pdf/2102.05740.pdf ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** We provide two types of tutorial notebooks\n",
    "- **Kats 101**, basic data structure and functionalities in Kats \n",
    "- **Kats 20x**, advanced topics, including advanced forecasting techniques, advanced detection algorithms, `TsFeatures`, meta-learning, etc. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Overview of Meta-Learning Framework For Forecasting\n",
    "\n",
    "Suppose we have a time series and we are looking to build the best possible forecast (with respect to a predefined error metric such as mean absolute error) from the following list of candidate models (and possibly other forecasting models in Kats too):\n",
    "* ARIMA\n",
    "* SARIMA\n",
    "* Holt-Winters\n",
    "* Prophet\n",
    "* Theta\n",
    "* STLF\n",
    "\n",
    "\n",
    "\n",
    "For a single time series, it is straightforward to to do hyperparameter tuning for each of the candidate models with this time series, calculate the error metric, and choose the model that minimizes the error metric.  We have discussed this methodology in detail in Kats 201.  Our basic metadata object, `GetMetaData`, which we will introduce below, also does this calculation to find the best forecasting model for a single time series.\n",
    "\n",
    "However, when we are working with a large number of time series, repeating this process quickly becomes intractable, and for that, we include a meta-learning framework for forecasting.  There are two key model classes, plus one optional one, in our meta-learning framework:\n",
    "\n",
    "1. `MetaLearnModelSelect`: Given the metadata for a time series, predict the best model family (from the candidate models of interest) to forecast the series.  This model is a random forest by default.\n",
    "2. `MetaLearnHPT`: Given a time series and a model type, predict the best parameters for this model.  This model is a neutral network.  \n",
    "3. `MetaLearnPredictability` (optional): Given the metadata for a time series, predict if it is \"predictable\", i.e. if it is possible to forecast with a threshold error.  This model is a random forest by default.\n",
    "\n",
    "For each of these models, you can use labeled training data to build a model or you load a pre-trained model from a file path.  \n",
    "\n",
    "We use the `GetMetaData` object to represent the metadata for a time series in `MetaLearnModelSelect` and `MetaLearnPredictability`.  This tutorial begins with an introduction to the `GetMetaData` object.  Since this object is heavily dependent on `TsFeatures`, if you are not familiar `TsFeatures`, you should check out Kats 203 prior to continuing with this tutorial.  \n",
    "\n",
    "Next we will use labeled time series data from the `m3_meta_data.csv` file to show how to use the `MetaLearnPredictability`, `MetaLearnModelSelect` and `MetaLearnPredictability`.  \n",
    "\n",
    "\n",
    "The sample data in `m3_meta_data.csv` is very small, with 78 labeled examples, so the examples we provide here will not be highly accurate, but they will show you the proper workflow for using the meta-learning framework for forecasting in Kats.\n",
    "\n",
    "\n",
    "The full table of contents for Kats 204 is as follow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Introduction to `GetMetaData`\n",
    "\n",
    "The `GetMetaData` class generates the metadata for any time series.  There are three key components to the the metadata for a time series:\n",
    "1. `features`: the `TsFeatures` dictionary for the time series\n",
    "2. `hpt_res`: a dictionary giving the best hyperparameters for each candidate model and the corresponding error metric for the time series \n",
    "3. `best_model`: the name of the model with the smallest error metric\n",
    "\n",
    "The default error metric is mean absolute error (mae) but this can be controlled with the `error_method` argument in `GetMetaData`.  \n",
    "\n",
    "The list of candidate models that we consider is controlled by the `all_models` argument in `GetMetaData`, which is a dictionary with string names of the candidate models as keys and corresponding model classes as value.\n",
    "\n",
    "with keys equal to the string names of the models as keys  and values equal to the corresponding model class.  The keys in `hpt_res` and the value of `best_model` come from the keys of the `all_models` dictionary.  The default value of `all_models` will include the following six models.\n",
    "\n",
    "1. ARIMA\n",
    "2. SARIMA\n",
    "3. Holt-Winters\n",
    "4. Prophet`\n",
    "5. Theta\n",
    "6. STLF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our first example uses the `air_passengers` data set.  We show how to get the metadata for this time series.  We start by loading the time series into a `TimeSeriesData` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "# For Google Colab:\n",
    "!pip install kats\n",
    "!wget https://raw.githubusercontent.com/facebookresearch/Kats/main/kats/data/air_passengers.csv\n",
    "!wget https://raw.githubusercontent.com/facebookresearch/Kats/main/kats/data/m3_meta_data.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sys\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "\n",
    "warnings.simplefilter(action='ignore')\n",
    "sys.path.append(\"../\")\n",
    "\n",
    "from kats.consts import TimeSeriesData\n",
    "\n",
    "try: # If running on Jupyter\n",
    "    air_passengers_df = pd.read_csv(\"../kats/data/air_passengers.csv\")\n",
    "except FileNotFoundError: # If running on colab\n",
    "    air_passengers_df = pd.read_csv(\"air_passengers.csv\")\n",
    "    \n",
    "air_passengers_df.columns = [\"time\", \"value\"]\n",
    "air_passengers_ts = TimeSeriesData(air_passengers_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can construct the `GetMetaData` object for the `air_passengers` data set time series like follows.  We use all of the default settings except that we use mean absolute percentage error (mape) as our error metric rather than the default of mean absolute error (mae) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from kats.models.metalearner.get_metadata import GetMetaData\n",
    "\n",
    "# create an object MD of class GetMetaData with error method mean absolute percentage error (mape)\n",
    "MD = GetMetaData(data=air_passengers_ts, error_method='mape')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the `all_models` dictionary that is used by default here.  You are allowed to specify your own `all_models` dictionary as long as all the values are classes that extends the abstract class `kats.models.Model`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'arima': kats.models.arima.ARIMAModel,\n",
       " 'holtwinters': kats.models.holtwinters.HoltWintersModel,\n",
       " 'prophet': kats.models.prophet.ProphetModel,\n",
       " 'theta': kats.models.theta.ThetaModel,\n",
       " 'stlf': kats.models.stlf.STLFModel,\n",
       " 'sarima': kats.models.sarima.SARIMAModel}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MD.all_models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `all_params` dictionary will have the same keys as the `all_models` dictionary, and the values are the corresponding parameter class (i.e. a class that extends the class `kats.const.Params`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'arima': kats.models.arima.ARIMAParams,\n",
       " 'holtwinters': kats.models.holtwinters.HoltWintersParams,\n",
       " 'prophet': kats.models.prophet.ProphetParams,\n",
       " 'theta': kats.models.theta.ThetaParams,\n",
       " 'stlf': kats.models.stlf.STLFParams,\n",
       " 'sarima': kats.models.sarima.SARIMAParams}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MD.all_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use the `get_meta_data` function to calculate all the metadata and output the result as a dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
      "\n",
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      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
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      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
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      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
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      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/base/model.py:547: HessianInversionWarning:\n",
      "\n",
      "Inverting hessian failed, no bse or cov_params available\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
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      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/base/model.py:547: HessianInversionWarning:\n",
      "\n",
      "Inverting hessian failed, no bse or cov_params available\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
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      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "WARNING:fbprophet.models:Optimization terminated abnormally. Falling back to Newton.\n",
      "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/base/model.py:547: HessianInversionWarning:\n",
      "\n",
      "Inverting hessian failed, no bse or cov_params available\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/base/model.py:566: ConvergenceWarning:\n",
      "\n",
      "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "\n",
      "WARNING:fbprophet.models:Optimization terminated abnormally. Falling back to Newton.\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/base/model.py:547: HessianInversionWarning:\n",
      "\n",
      "Inverting hessian failed, no bse or cov_params available\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
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      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
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      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
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      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/base/model.py:566: ConvergenceWarning:\n",
      "\n",
      "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/base/model.py:566: ConvergenceWarning:\n",
      "\n",
      "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "WARNING:fbprophet.models:Optimization terminated abnormally. Falling back to Newton.\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/base/model.py:566: ConvergenceWarning:\n",
      "\n",
      "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/base/model.py:566: ConvergenceWarning:\n",
      "\n",
      "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/base/model.py:566: ConvergenceWarning:\n",
      "\n",
      "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/base/model.py:566: ConvergenceWarning:\n",
      "\n",
      "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/base/model.py:566: ConvergenceWarning:\n",
      "\n",
      "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/base/model.py:566: ConvergenceWarning:\n",
      "\n",
      "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "\n",
      "WARNING:fbprophet.models:Optimization terminated abnormally. Falling back to Newton.\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/base/model.py:547: HessianInversionWarning:\n",
      "\n",
      "Inverting hessian failed, no bse or cov_params available\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/base/model.py:566: ConvergenceWarning:\n",
      "\n",
      "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
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      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
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      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/base/model.py:547: HessianInversionWarning:\n",
      "\n",
      "Inverting hessian failed, no bse or cov_params available\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/base/model.py:547: HessianInversionWarning:\n",
      "\n",
      "Inverting hessian failed, no bse or cov_params available\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/base/model.py:547: HessianInversionWarning:\n",
      "\n",
      "Inverting hessian failed, no bse or cov_params available\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/base/model.py:547: HessianInversionWarning:\n",
      "\n",
      "Inverting hessian failed, no bse or cov_params available\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n",
      "/usr/local/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:524: ValueWarning:\n",
      "\n",
      "No frequency information was provided, so inferred frequency MS will be used.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# get meta data as a dictionary\n",
    "air_passengers_metadata = MD.get_meta_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the keys of the metadata dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['hpt_res', 'features', 'best_model', 'search_method', 'error_method'])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_passengers_metadata.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We explained what `features`, `hpt_res` and `best_model` are above.  This dictionary also includes the `search_method` and `error_method`, which will just be the default values in this case.  We can see these as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "search_method: RandomSearch\n",
      "error_method: mape\n"
     ]
    }
   ],
   "source": [
    "print(f\"search_method: {air_passengers_metadata['search_method']}\")\n",
    "print(f\"error_method: {air_passengers_metadata['error_method']}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The keys of the `hpt_res` dictionary are name of the candidate model families; they should be the same as the keys for the `all_models` and `all_parameters` dictionaries. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['arima', 'holtwinters', 'prophet', 'theta', 'stlf', 'sarima'])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_passengers_metadata['hpt_res'].keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The values of the `hpt_res` dictionary are two-element tuples.  The first element is gives the hyperparameters that minimize the error metric.  The second element gives the corresponding minimum error metric.  Let's take a look at these values for ARIMA:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'p': 3, 'd': 1, 'q': 3}, 0.09311025158863304)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_passengers_metadata['hpt_res']['arima']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can sort the different methods by their error metric as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['prophet', 'stlf', 'holtwinters', 'arima', 'sarima', 'theta']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "methods = list(air_passengers_metadata['hpt_res'].keys())\n",
    "sorted(methods, key = lambda m: air_passengers_metadata['hpt_res'][m][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This suggests that Prophet has the lowest error metric.  Let's confirm that this is what `best_model` indicates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'prophet'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_passengers_metadata['best_model']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We constructed the `GetMetaData` object for the `air_passengers` data set with all of the default settings.  Let's take a look at the full set of attributes that can be used to initialize `GetMetadata`.\n",
    "\n",
    "This is the only required attributed: \n",
    "* **data**: TimeSeriesData, the time series for which we calculate the metadata\n",
    "\n",
    "The following attributes are all optional:\n",
    "* **all_models**: `Dict[str, m.Model]`, a dictionary for the candidate model classes.  The key is a string naming the model and each value is a corresponding model class (i.e. a class that extends the abstract class `kats.models.Model`).\n",
    "* **all_params**: `Dict[str, Params]`, a dictionary for the candidate model parameter classes.  The keys are the same as the keys for `all_models` and each value is a corresponding parameter class (i.e. a class that extends the class `kats.const.Params`).\n",
    "* **min_length**: int, the minimal length of time series. We raise a value error if the length of `data` is smaller than `min_length`.  The default value of `min_length` is 30.\n",
    "* **scale**: bool, Whether to rescale the time series by its maximum value; default is true.\n",
    "* **method**: SearchMethodEnum, Search method for hyper-parameters tuning; default is random search in the default parameter space\n",
    "* **executor**: Callable, A parallel executor for parallel processing.  By default, we use Python's native multiprocessing implementation.\n",
    "* **error_method**: str, Type of error metric.  Options are `'mape`', `'smape`',`'mae`', `'mase`', `'mse`', `'rmse`';  default is `'mae'`.\n",
    "* **num_trials**: int, Number of trials for hyperparameter search; default is 5\n",
    "* **num_arm**: optional Number of arms in hyperparameter search; default is 4."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the remaining examples, we use the sample data in `m3_meta_data.csv` to show how to build meta-learning models.  This sample data set contains the metadata for 78 time series, meaning it has that we need to construct 78 metadata dictionaries like the one we constructed for the `air_passengers` data set.  While 78 metadata objects is certainly too few to develop an accurate meta-learning model and you should use more examples for your own meta-learning models to get high accuracy, these examples will help familiarize you with our meta-learning framework.\n",
    "\n",
    "Loading this data is straightforward is straightforward.  After loading it into a `DataFrame`, we have to do some pre-processing with the `eval` function to ensure that the dictionaries are represented as dictionaries and not as strings.  We demonstrate this as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the metadata into a DataFrame\n",
    "try: # If running on Jupyter\n",
    "    metadata_df = pd.read_csv(\"../kats/data/m3_meta_data.csv\")\n",
    "except FileNotFoundError: # If running on colab\n",
    "    metadata_df = pd.read_csv(\"m3_meta_data.csv\")\n",
    "\n",
    "# We need to do a little pre-processing to make sure the dictionaries are represented as dictionaries\n",
    "# rather than as strings.  This function will do that pre-processing.\n",
    "def change_format(tmp):\n",
    "    tmp['hpt_res']=eval(tmp['hpt_res'])\n",
    "    tmp['hpt_res']['sarima'][0]['seasonal_order'] = eval(tmp['hpt_res']['sarima'][0]['seasonal_order'])\n",
    "    tmp['features']=eval(tmp['features'])\n",
    "    return tmp\n",
    "\n",
    "metadata_df = metadata_df.apply(change_format, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's preview the metadata `DataFrame` we just loaded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hpt_res</th>\n",
       "      <th>features</th>\n",
       "      <th>best_model</th>\n",
       "      <th>search_method</th>\n",
       "      <th>error_method</th>\n",
       "      <th>idx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'arima': ({'p': 5, 'd': 2, 'q': 5}, 0.8932924...</td>\n",
       "      <td>{'length': 68, 'mean': 0.35392156862745106, 'v...</td>\n",
       "      <td>stlf</td>\n",
       "      <td>RandomSearch</td>\n",
       "      <td>mape</td>\n",
       "      <td>N1402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{'arima': ({'p': 5, 'd': 2, 'q': 1}, 0.3989958...</td>\n",
       "      <td>{'length': 68, 'mean': 0.19995256166982928, 'v...</td>\n",
       "      <td>arima</td>\n",
       "      <td>RandomSearch</td>\n",
       "      <td>mape</td>\n",
       "      <td>N1403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{'arima': ({'p': 1, 'd': 2, 'q': 3}, 0.2519500...</td>\n",
       "      <td>{'length': 68, 'mean': 0.48962530217566486, 'v...</td>\n",
       "      <td>holtwinters</td>\n",
       "      <td>RandomSearch</td>\n",
       "      <td>mape</td>\n",
       "      <td>N1404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>{'arima': ({'p': 2, 'd': 1, 'q': 3}, 0.3146896...</td>\n",
       "      <td>{'length': 68, 'mean': 0.2980870396939263, 'va...</td>\n",
       "      <td>stlf</td>\n",
       "      <td>RandomSearch</td>\n",
       "      <td>mape</td>\n",
       "      <td>N1405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>{'arima': ({'p': 3, 'd': 1, 'q': 1}, 0.1448053...</td>\n",
       "      <td>{'length': 68, 'mean': 0.5336050082683677, 'va...</td>\n",
       "      <td>arima</td>\n",
       "      <td>RandomSearch</td>\n",
       "      <td>mape</td>\n",
       "      <td>N1406</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             hpt_res  \\\n",
       "0  {'arima': ({'p': 5, 'd': 2, 'q': 5}, 0.8932924...   \n",
       "1  {'arima': ({'p': 5, 'd': 2, 'q': 1}, 0.3989958...   \n",
       "2  {'arima': ({'p': 1, 'd': 2, 'q': 3}, 0.2519500...   \n",
       "3  {'arima': ({'p': 2, 'd': 1, 'q': 3}, 0.3146896...   \n",
       "4  {'arima': ({'p': 3, 'd': 1, 'q': 1}, 0.1448053...   \n",
       "\n",
       "                                            features   best_model  \\\n",
       "0  {'length': 68, 'mean': 0.35392156862745106, 'v...         stlf   \n",
       "1  {'length': 68, 'mean': 0.19995256166982928, 'v...        arima   \n",
       "2  {'length': 68, 'mean': 0.48962530217566486, 'v...  holtwinters   \n",
       "3  {'length': 68, 'mean': 0.2980870396939263, 'va...         stlf   \n",
       "4  {'length': 68, 'mean': 0.5336050082683677, 'va...        arima   \n",
       "\n",
       "  search_method error_method    idx  \n",
       "0  RandomSearch         mape  N1402  \n",
       "1  RandomSearch         mape  N1403  \n",
       "2  RandomSearch         mape  N1404  \n",
       "3  RandomSearch         mape  N1405  \n",
       "4  RandomSearch         mape  N1406  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's convert this metadata `DataFrame` into a list of metadata dictionaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_list = metadata_df.to_dict(orient='records')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Determining Predictability with `MetaLearnPredictability`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before using meta-learning models for model selection and hyper-parameter forecasting, we would like to know if our target time series is predictable.  The `MetaLearnPredictability` module allows us to treat this like a binary classification problem and build a model for it.  We train this model using a list of a metadata and a threshold for the error metric.  We use the threshold to label each metadata dictionary as predictable if and only if the error of it's `best_model` is smaller than the input threshold.  The arguments for `MetaLearnPredictability` are as follows:\n",
    "\n",
    "\n",
    "* **metadata**: A list of dictionaries representing the meta-data of time series (e.g., the meta-data generated by GetMetaData object).  Required unless `load_model=True`.\n",
    "\n",
    "* **threshold**: Float; the threshold for the forecasting error. A time series whose forecasting error of the best forecasting model is higher than the threshold is considered as unpredictable. Default is 0.2.\n",
    "\n",
    "* **load_model**: Boolean; whether or not to load a trained model. Default is False.\n",
    "\n",
    "\n",
    "If we want to train a new predictability model from a list of metadata dictionaries, we should include that list in the `metadata` argument.  If we want to load a trained model, we set `load_data=True` and do ignore the `metadata` argument.  We will provide examples of both below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our example, we are going to use the sample metadata from the `m3_meta_data.csv` file to train a predictability model with `MetaLearnPredictability`.  Then we will use this to predict whether or not `air_passenger` time series can be forecasted (with MAPE at most 0.2).\n",
    "\n",
    "We initialize model using the `metadata_list` we previously generated from `m3_meta_data.csv` as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from kats.models.metalearner.metalearner_predictability import MetaLearnPredictability\n",
    "\n",
    "# take the time series with MAPE>=0.2 as unpreditable time series and initial the object\n",
    "mlp=MetaLearnPredictability(metadata_list, threshold=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we train the model, we see a dictionary with performance metrics calculated on the test data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Fail to get a proper threshold for recall 0.7, use 0.5 as threshold instead. Exception message is: index 6 is out of bounds for axis 0 with size 6\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.8571428571428571,\n",
       " 'precision': 0.8333333333333334,\n",
       " 'recall': 1.0,\n",
       " 'f1': 0.9090909090909091}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use this model to predict if the `air_passenger` time series is predictable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp.pred(air_passengers_ts) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This suggests that this model can be forecast with MAPE at most 0.2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's save the model we trained to a file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlp.save_model(\"mlp.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's re-load our saved model into a new `MetaLearnPredictability` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#initiate a new object and load the trained model\n",
    "mlp2 = MetaLearnPredictability(load_model=True)\n",
    "mlp2.load_model(\"mlp.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's use our newly loaded model to repeat the prediction we did on the `air_passenger` data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp2.pred(air_passengers_ts) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. **Model Selection with `MetaLearnModelSelect`**\n",
    "\n",
    "The `MetaLearnModelSelect` object allows you to build a predictive model to determine the best forecasting model for a time series.  It is trained using a list of metadata dictionaries.  The arguments for `MetaLearnModelSelect` are as follows:\n",
    "\n",
    "* **metadata**: A list of dictionaries representing the meta-data of time series (e.g., the meta-data generated by GetMetaData object).  Required unless `load_model=True`.\n",
    "\n",
    "* **load_model**: Boolean; whether or not to load a trained model. Default is False.\n",
    "\n",
    "If we want to train a new predictability model from a list of metadata dictionaries, we should include that list in the `metadata` argument.  If we want to load a trained model, we set `load_data=True` and do ignore the `metadata` argument.  We will provide examples of both below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our example, we are going to use the sample metadata from the `m3_meta_data.csv` file to train a selection model with `MetaLearnModelSelect`.  Then we will use this to predict the best forecasting model for the `air_passenger` time series.\n",
    "\n",
    "We initialize model using the `metadata_list` we previously generated from `m3_meta_data.csv` as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from kats.models.metalearner.metalearner_modelselect import MetaLearnModelSelect\n",
    "\n",
    "#Initialize the MetaLearnModelSelect object\n",
    "mlms = MetaLearnModelSelect(metadata_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each metadata dictionary includes a `best_model`, and we can take a look at the frquencies of these models using the `count_category` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'stlf': 22,\n",
       "         'arima': 11,\n",
       "         'holtwinters': 16,\n",
       "         'prophet': 7,\n",
       "         'sarima': 17,\n",
       "         'theta': 5})"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlms.count_category()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we visualize the data and train the model, it is helpful do some preprocessing.  We can do this with the `preprocess` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pre-process the metadata\n",
    "# don't down-sample it to balance the classes\n",
    "# standardize the TsFeatures to have zero mean and unit variance\n",
    "mlms.preprocess(downsample=False, scale=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see how the different `TsFeatures` in our metadata objects are correlated with each other by plotting a heatmap, which can be generated using the `plot_corr_heatmap` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mlms.plot_corr_heatmap()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, it is time to train our model.  By default, we will be fitting a random forest model, but other model types (including GBDT, SVT, KNN, Naive Bayes) can be supported using the `method` parameter in the `train` function.  When we run the `train` function, it outputs a dictionary with the training error and test error for each of the candidate models.  All of these error metrics are MAPE because that is the error metric our metadata is using for this example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'fit_error': {'meta-learn': 0.2638633442229436,\n",
       "  'stlf': 0.30970554036192316,\n",
       "  'arima': 0.37546320032072317,\n",
       "  'holtwinters': 0.34828296078095333,\n",
       "  'prophet': 0.3327356135179752,\n",
       "  'sarima': 0.30737435743886254,\n",
       "  'theta': 0.36688102394165684},\n",
       " 'pred_error': {'meta-learn': 0.2933239469671236,\n",
       "  'stlf': 0.2875451486512929,\n",
       "  'arima': 0.27918433393750763,\n",
       "  'holtwinters': 0.31147608067756816,\n",
       "  'prophet': 0.28725389558871767,\n",
       "  'sarima': 0.2836928352084283,\n",
       "  'theta': 0.30491135485342835},\n",
       " 'clf_accuracy': 0.25}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train a modelselect model using random forest algorithm   \n",
    "results=mlms.train()\n",
    "\n",
    "# preview the dictionary\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's view this dictionary as a `DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>meta-learn</th>\n",
       "      <th>stlf</th>\n",
       "      <th>arima</th>\n",
       "      <th>holtwinters</th>\n",
       "      <th>prophet</th>\n",
       "      <th>sarima</th>\n",
       "      <th>theta</th>\n",
       "      <th>error_type</th>\n",
       "      <th>error_metric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.263863</td>\n",
       "      <td>0.309706</td>\n",
       "      <td>0.375463</td>\n",
       "      <td>0.348283</td>\n",
       "      <td>0.332736</td>\n",
       "      <td>0.307374</td>\n",
       "      <td>0.366881</td>\n",
       "      <td>fit_error</td>\n",
       "      <td>MAPE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.293324</td>\n",
       "      <td>0.287545</td>\n",
       "      <td>0.279184</td>\n",
       "      <td>0.311476</td>\n",
       "      <td>0.287254</td>\n",
       "      <td>0.283693</td>\n",
       "      <td>0.304911</td>\n",
       "      <td>pred_error</td>\n",
       "      <td>MAPE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   meta-learn      stlf     arima  holtwinters   prophet    sarima     theta  \\\n",
       "0    0.263863  0.309706  0.375463     0.348283  0.332736  0.307374  0.366881   \n",
       "1    0.293324  0.287545  0.279184     0.311476  0.287254  0.283693  0.304911   \n",
       "\n",
       "   error_type error_metric  \n",
       "0   fit_error         MAPE  \n",
       "1  pred_error         MAPE  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df=pd.DataFrame([results['fit_error'], results['pred_error']])\n",
    "results_df['error_type']=['fit_error', 'pred_error']\n",
    "results_df['error_metric']='MAPE'\n",
    "results_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's use our trained model to predict the best model for the `air_passengers` time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'stlf'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlms.pred(air_passengers_ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's save the model we trained to a file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlms.save_model(\"mlms.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's re-load our saved model into a new `MetaLearnModelSelect` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlms2 = MetaLearnModelSelect(load_model=True)\n",
    "mlms2.load_model(\"mlms.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's use our newly loaded model to repeat the prediction we did on the `air_passenger` data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'stlf'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlms2.pred(air_passengers_ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. **Hyperparameter Tuning with `MetaLearnHPT`**\n",
    "\n",
    "The `MetaLearnHPT` object allows you to build a model to predict the best hyperparameters for a time series given a designated forecasting model.  Specifically, `MetaLearnHPT` builds a neural network model that takes the `TsFeatures` for a time series as inputs and predicts the best hyperparameters for the forecasting model.\n",
    "\n",
    "Since a metadata dictionary contains both the `TsFeatures` and the best parameters (with keys `features` and `hpt_res`, respectively), we can use a list of metadata dictionaries to build this predictive model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our example, we use `metadata_list`, which contains the metadata from the `m3_meta_data.csv` file, to build a  model for the Holt-Winters parameters for a time series.  We then use this model to predict the best Holt-Winters parameters for the `air_passengers` time series.  While this example is using the Holt-Winters model as the designated model, the same process can be used for any forecasting model supported by Kats as long as it is included in our metadata objects."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 Initializing `MetaLearnHPT`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To initialize the `MetaLearnHPT` model, we need to input the `TsFeatures` and hyperparameters for the Holt-Winters model as `DataFrame` objects.  To extract these from the metadata from `m3_meta_data.csv`, it is easiest use the `DataFrame` we loaded with this data, `metadata_df`.\n",
    "\n",
    "First, let's load the `TsFeatures` from `metadata_df` to a new `DataFrame` and preview it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>length</th>\n",
       "      <th>mean</th>\n",
       "      <th>var</th>\n",
       "      <th>entropy</th>\n",
       "      <th>lumpiness</th>\n",
       "      <th>stability</th>\n",
       "      <th>flat_spots</th>\n",
       "      <th>hurst</th>\n",
       "      <th>std1st_der</th>\n",
       "      <th>crossing_points</th>\n",
       "      <th>...</th>\n",
       "      <th>diff2y_pacf5</th>\n",
       "      <th>seas_acf1</th>\n",
       "      <th>seas_pacf1</th>\n",
       "      <th>firstmin_ac</th>\n",
       "      <th>firstzero_ac</th>\n",
       "      <th>holt_alpha</th>\n",
       "      <th>holt_beta</th>\n",
       "      <th>hw_alpha</th>\n",
       "      <th>hw_beta</th>\n",
       "      <th>hw_gamma</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>68</td>\n",
       "      <td>0.353922</td>\n",
       "      <td>0.045430</td>\n",
       "      <td>0.833179</td>\n",
       "      <td>0.000461</td>\n",
       "      <td>0.011440</td>\n",
       "      <td>2</td>\n",
       "      <td>0.067969</td>\n",
       "      <td>0.138205</td>\n",
       "      <td>32</td>\n",
       "      <td>...</td>\n",
       "      <td>5.088309</td>\n",
       "      <td>0.212199</td>\n",
       "      <td>0.071177</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0.126924</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.328909e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.186695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68</td>\n",
       "      <td>0.199953</td>\n",
       "      <td>0.027798</td>\n",
       "      <td>0.899969</td>\n",
       "      <td>0.000377</td>\n",
       "      <td>0.001403</td>\n",
       "      <td>3</td>\n",
       "      <td>0.031521</td>\n",
       "      <td>0.113224</td>\n",
       "      <td>33</td>\n",
       "      <td>...</td>\n",
       "      <td>1.407981</td>\n",
       "      <td>-0.085866</td>\n",
       "      <td>-0.060512</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.034220</td>\n",
       "      <td>0.032252</td>\n",
       "      <td>1.052632e-01</td>\n",
       "      <td>1.052632e-01</td>\n",
       "      <td>0.263158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68</td>\n",
       "      <td>0.489625</td>\n",
       "      <td>0.045509</td>\n",
       "      <td>0.806731</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.014501</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.040535</td>\n",
       "      <td>0.129866</td>\n",
       "      <td>24</td>\n",
       "      <td>...</td>\n",
       "      <td>1.342792</td>\n",
       "      <td>0.173154</td>\n",
       "      <td>-0.019452</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>0.044327</td>\n",
       "      <td>0.044327</td>\n",
       "      <td>1.052632e-01</td>\n",
       "      <td>1.052632e-01</td>\n",
       "      <td>0.210526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>68</td>\n",
       "      <td>0.298087</td>\n",
       "      <td>0.049313</td>\n",
       "      <td>0.667211</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.026464</td>\n",
       "      <td>1</td>\n",
       "      <td>0.021439</td>\n",
       "      <td>0.094965</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>1.676189</td>\n",
       "      <td>0.330326</td>\n",
       "      <td>-0.217522</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.263158e-02</td>\n",
       "      <td>5.263158e-02</td>\n",
       "      <td>0.157895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68</td>\n",
       "      <td>0.533605</td>\n",
       "      <td>0.065904</td>\n",
       "      <td>0.823098</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>0.021781</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.045258</td>\n",
       "      <td>0.152166</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>1.469424</td>\n",
       "      <td>0.207897</td>\n",
       "      <td>0.014251</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.091354e-10</td>\n",
       "      <td>9.692337e-12</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   length      mean       var   entropy  lumpiness  stability  flat_spots  \\\n",
       "0      68  0.353922  0.045430  0.833179   0.000461   0.011440           2   \n",
       "1      68  0.199953  0.027798  0.899969   0.000377   0.001403           3   \n",
       "2      68  0.489625  0.045509  0.806731   0.000044   0.014501           2   \n",
       "3      68  0.298087  0.049313  0.667211   0.000149   0.026464           1   \n",
       "4      68  0.533605  0.065904  0.823098   0.000124   0.021781           2   \n",
       "\n",
       "      hurst  std1st_der  crossing_points  ...  diff2y_pacf5  seas_acf1  \\\n",
       "0  0.067969    0.138205               32  ...      5.088309   0.212199   \n",
       "1  0.031521    0.113224               33  ...      1.407981  -0.085866   \n",
       "2 -0.040535    0.129866               24  ...      1.342792   0.173154   \n",
       "3  0.021439    0.094965               25  ...      1.676189   0.330326   \n",
       "4 -0.045258    0.152166               21  ...      1.469424   0.207897   \n",
       "\n",
       "   seas_pacf1  firstmin_ac  firstzero_ac  holt_alpha  holt_beta      hw_alpha  \\\n",
       "0    0.071177            1            12    0.126924   0.000000  2.328909e-01   \n",
       "1   -0.060512            1             3    0.034220   0.032252  1.052632e-01   \n",
       "2   -0.019452            2            18    0.044327   0.044327  1.052632e-01   \n",
       "3   -0.217522            3            23    0.000007   0.000000  5.263158e-02   \n",
       "4    0.014251            2            22    0.000064   0.000000  7.091354e-10   \n",
       "\n",
       "        hw_beta  hw_gamma  \n",
       "0  0.000000e+00  0.186695  \n",
       "1  1.052632e-01  0.263158  \n",
       "2  1.052632e-01  0.210526  \n",
       "3  5.263158e-02  0.157895  \n",
       "4  9.692337e-12  0.000000  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata_features_df = pd.DataFrame(metadata_df['features'].tolist())\n",
    "metadata_features_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's do the same for the the Holt-Winters hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seasonal_periods</th>\n",
       "      <th>trend</th>\n",
       "      <th>damped</th>\n",
       "      <th>seasonal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>additive</td>\n",
       "      <td>True</td>\n",
       "      <td>multiplicative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>multiplicative</td>\n",
       "      <td>True</td>\n",
       "      <td>multiplicative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>additive</td>\n",
       "      <td>True</td>\n",
       "      <td>additive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>multiplicative</td>\n",
       "      <td>False</td>\n",
       "      <td>multiplicative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>multiplicative</td>\n",
       "      <td>False</td>\n",
       "      <td>additive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   seasonal_periods           trend  damped        seasonal\n",
       "0                 7        additive    True  multiplicative\n",
       "1                 7  multiplicative    True  multiplicative\n",
       "2                14        additive    True        additive\n",
       "3                 4  multiplicative   False  multiplicative\n",
       "4                10  multiplicative   False        additive"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata_hpt_df = pd.DataFrame(metadata_df['hpt_res'].map(lambda x: x['holtwinters'][0]).tolist())\n",
    "metadata_hpt_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The arguments for `MetaLearnHPT` are:\n",
    "* **data_x**: pd.DataFrame; A DataFrame with the TsFeatures.  Required unless `load_model=True`.\n",
    "* **data_y**: pd.DataFrame; A DataFrame with the best hyperparameters.  Required unless `load_model=True`.\n",
    "* **default_model**: string; The name of the forecast model whose default settings will be used. Supported options are 'arima', 'sarima', 'theta', 'prophet', 'holtwinters', 'stlf' and None.  Default is None, in which case we instantiate a custom model and use `categorical_idx` and `numerical_idx` to get the names of the hyperparameters.\n",
    "* **categorical_idx**: A list of strings of the names of the categorical hyper-parameters.  Required only when `default_model` is `None` and there are categorical hyper-parameters.\n",
    "* **numerical_idx**: Optional; A list of strings of the names of the numerical hyper-parameters.  Required only when `default_model` is `None` and there are numerical hyper-parameters.\n",
    "    If None, then a customized model will be initiated.\n",
    "* **load_model**: Boolean; whether or not to load a trained model. Default is False.\n",
    "\n",
    "\n",
    "We can initialize the `MetaLearnHPT` model using a `default_model` as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from kats.models.metalearner.metalearner_hpt import MetaLearnHPT\n",
    "\n",
    "mlhpt_holtwinters = MetaLearnHPT(\n",
    "    data_x=metadata_features_df,\n",
    "    data_y=metadata_hpt_df,\n",
    "    default_model='holtwinters'\n",
    ")"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "We can also initialize this as a custom model by specifying the names of the parameters rather than the name of the model.  The main difference between these formulations is as follows:\n",
    "\n",
    "* When using a default model, you need to use the default neural network structure \n",
    "* When using a custom model, you need to specify the neural network parameters.\n",
    "\n",
    "We will show an example of each of these"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlhpt_holtwinters2=MetaLearnHPT(\n",
    "    data_x=metadata_features_df,\n",
    "    data_y=metadata_hpt_df,\t\n",
    "    categorical_idx = [\"trend\",\"damped\",\"seasonal\"],\n",
    "    numerical_idx = [\"seasonal_periods\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 `MetaLearnHPT` with Default Neural Network Model Structure\n",
    "\n",
    "When using a default model like we did when initializing `mlhpt_holtwinters`, `MetaLearnHPT` builds a neural network with the default neural network model structure.  This means we call the `build_network` function with no parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multi-task neural network structure:\n",
      "MultitaskNet(\n",
      "  (shared_layer): ModuleList(\n",
      "    (0): Linear(in_features=40, out_features=20, bias=True)\n",
      "  )\n",
      "  (cat_layer_combo): ModuleList(\n",
      "    (0): ModuleList(\n",
      "      (0): Linear(in_features=20, out_features=2, bias=True)\n",
      "      (1): Linear(in_features=2, out_features=2, bias=True)\n",
      "    )\n",
      "    (1): ModuleList(\n",
      "      (0): Linear(in_features=20, out_features=3, bias=True)\n",
      "      (1): Linear(in_features=3, out_features=2, bias=True)\n",
      "    )\n",
      "    (2): ModuleList(\n",
      "      (0): Linear(in_features=20, out_features=5, bias=True)\n",
      "      (1): Linear(in_features=5, out_features=2, bias=True)\n",
      "    )\n",
      "    (3): ModuleList(\n",
      "      (0): Linear(in_features=20, out_features=3, bias=True)\n",
      "      (1): Linear(in_features=3, out_features=6, bias=True)\n",
      "    )\n",
      "  )\n",
      "  (num_layer): ModuleList()\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "mlhpt_holtwinters.build_network()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the `train` function to train the neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlhpt_holtwinters.train(lr=0.001, batch_size=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the training curves for this model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mlhpt_holtwinters.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's use our trained model to predict the best Holt-Winters parameters for the `air_passengers` time series.  The `pred` function returns a `DataFrame` and the predicted parameters are in the `parameters` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'trend': 'multiplicative',\n",
       " 'damped': False,\n",
       " 'seasonal': 'multiplicative',\n",
       " 'seasonal_periods': 7}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred=mlhpt_holtwinters.pred(air_passengers_ts)\n",
    "pred['parameters'].iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's save the model we trained to a file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlhpt_holtwinters.save_model(\"mlhpt_hw.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's re-load our saved model into a new `MetaLearnHPT` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlhpt_holtwinters3=MetaLearnHPT(load_model=True)\n",
    "mlhpt_holtwinters3.load_model(\"mlhpt_hw.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use our newly loaded model to repeat the prediction we did on the `air_passenger` data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'trend': 'multiplicative',\n",
       " 'damped': False,\n",
       " 'seasonal': 'multiplicative',\n",
       " 'seasonal_periods': 7}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred=mlhpt_holtwinters3.pred(air_passengers_ts)\n",
    "pred['parameters'].iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 `MetaLearnHPT` with Custom Neural Network Model Structure\n",
    "\n",
    "When using a custom model like we did when initializing `mlhpt_holtwinters2`, you need to specify the model structure by providing the parameters for the neural network to the `build_network` function. \n",
    "\n",
    "Here's how we can do that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multi-task neural network structure:\n",
      "MultitaskNet(\n",
      "  (shared_layer): ModuleList(\n",
      "    (0): Linear(in_features=40, out_features=50, bias=True)\n",
      "  )\n",
      "  (cat_layer_combo): ModuleList(\n",
      "    (0): ModuleList(\n",
      "      (0): Linear(in_features=50, out_features=20, bias=True)\n",
      "      (1): Linear(in_features=20, out_features=10, bias=True)\n",
      "      (2): Linear(in_features=10, out_features=2, bias=True)\n",
      "    )\n",
      "    (1): ModuleList(\n",
      "      (0): Linear(in_features=50, out_features=20, bias=True)\n",
      "      (1): Linear(in_features=20, out_features=10, bias=True)\n",
      "      (2): Linear(in_features=10, out_features=2, bias=True)\n",
      "    )\n",
      "    (2): ModuleList(\n",
      "      (0): Linear(in_features=50, out_features=20, bias=True)\n",
      "      (1): Linear(in_features=20, out_features=2, bias=True)\n",
      "    )\n",
      "  )\n",
      "  (num_layer): ModuleList(\n",
      "    (0): Linear(in_features=50, out_features=30, bias=True)\n",
      "    (1): Linear(in_features=30, out_features=1, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "mlhpt_holtwinters2.build_network(\n",
    "    #One shared one-layer NN with 50 neurons.\n",
    "    n_hidden_shared=[50],\n",
    "    #Each classification task has its own task-specific NN. In this example, \"trend\" and \"dampled\" both have a two-layer NN respectively\n",
    "    #and \"seasonal\" has a one-layer NN.\n",
    "    n_hidden_cat_combo=[[20, 10], [20, 10], [20]], \n",
    "    #One task-specific one-layer NN with 30 neurons for regression task.\n",
    "    n_hidden_num=[30]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's use the `train` function to train the model.  We include some of the extra parameters here to specify how to train the neural network model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train the customized NN\n",
    "mlhpt_holtwinters2.train(    \n",
    "    #loss_scale is used to balance 2 types of losses: cross-entropy for classification tasks and MSE for regression tasks\n",
    "    loss_scale=30,\n",
    "    #learning rate\n",
    "    lr=0.005,\n",
    "    n_epochs=2000,\n",
    "    batch_size=16,\n",
    "    #supports ADAM and SGD\n",
    "    method='SGD',\n",
    "    #momentum in SGD.\n",
    "    momentum=0,\n",
    "    #early stop option.\n",
    "    n_epochs_stop=50,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the training curves for this model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mlhpt_holtwinters2.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use our trained model to predict the best parameters for the `air_passengers` time series. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'trend': 'multiplicative',\n",
       " 'damped': False,\n",
       " 'seasonal': 'multiplicative',\n",
       " 'seasonal_periods': 145.38652}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred=mlhpt_holtwinters2.pred(air_passengers_ts)\n",
    "pred['parameters'].iloc[0]"
   ]
  }
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